This repository contains the Knowledge_graph_building.ipynb notebook, which is designed to demonstrate the process of building knowledge graphs using advanced natural language processing and machine learning techniques.
The notebook leverages a series of Python libraries to extract information, analyze text, and construct a knowledge graph from various data sources. It utilizes the MapReduce chain to process and combine documents, capitalizing on large language models to understand and structure knowledge.
- Extracting information from sources like Wikipedia. and local documents.
- Using large language models (LLM) for text analysis.
- Iterative construction of knowledge graphs.
- Visualization and summarization of results.
- openai for interacting with language models.
- langchain for blockchain logic and prompt templates.
- spacy_llm and collections for text processing and data handling.
Before running the notebook, ensure all necessary libraries are installed and any required API keys for openai are configured.
To start the knowledge graph building process, simply run all cells in the notebook. The notebook is structured into sections that include document loading, analysis, and graph construction.
Contributions to this project are welcome. Please see the open issues for current project needs or open a new issue to suggest improvements or report bugs.